Update the scope of third party tests with the latest changes (IntelP…

…ython#2148)

The PR proposes to update scope and content of third party tests with
the latest version.
That would help to reduce future work while implementing missing
functions and to improve code coverage of existing supported
functionality.

Also PR removes impacted tests from the files with a skip list and
replaces that by explicit muting in tests code.

.pre-commit-config.yaml

@@ -34,7 +34,8 @@ repos:
             (?x)^(
                 dpnp/tests/test_arraycreation.py|
                 dpnp/tests/test_sycl_queue.py|
-                dpnp/tests/test_usm_type.py
+                dpnp/tests/test_usm_type.py|
+                dpnp/tests/third_party/cupy/core_tests/test_nep50_examples.py
             )$
     -   id: python-no-log-warn
     -   id: python-use-type-annotations

dpnp/dpnp_array.py

@@ -150,7 +150,7 @@ def mT(self):
         if self.ndim < 2:
             raise ValueError("matrix transpose with ndim < 2 is undefined")
 
-        return self._array_obj.mT
+        return dpnp_array._create_from_usm_ndarray(self._array_obj.mT)
 
     def to_device(self, target_device):
         """Transfer array to target device."""

dpnp/random/dpnp_iface_random.py

@@ -1022,25 +1022,31 @@ def power(a, size=None):
     return call_origin(numpy.random.power, a, size)
 
 
-def rand(d0, *dn, device=None, usm_type="device", sycl_queue=None):
+def rand(*args, device=None, usm_type="device", sycl_queue=None):
     """
     Random values in a given shape.
 
-    Create an array of the given shape and populate it with random samples
-    from a uniform distribution over [0, 1).
+    Create an array of the given shape and populate it with random samples from
+    a uniform distribution over ``[0, 1)``.
 
     For full documentation refer to :obj:`numpy.random.rand`.
 
     Parameters
     ----------
+    *args : sequence of ints, optional
+        The dimensions of the returned array, must be non-negative.
+        If no argument is given a single Python float is returned.
     device : {None, string, SyclDevice, SyclQueue}, optional
         An array API concept of device where the output array is created.
-        The `device` can be ``None`` (the default), an OneAPI filter selector string,
-        an instance of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL device,
-        an instance of :class:`dpctl.SyclQueue`, or a `Device` object returned by
+        The `device` can be ``None`` (the default), an OneAPI filter selector
+        string, an instance of :class:`dpctl.SyclDevice` corresponding to
+        a non-partitioned SYCL device, an instance of :class:`dpctl.SyclQueue`,
+        or a `Device` object returned by
         :obj:`dpnp.dpnp_array.dpnp_array.device` property.
+        Default: ``None``.
     usm_type : {"device", "shared", "host"}, optional
         The type of SYCL USM allocation for the output array.
+        Default: ``"device"``.
     sycl_queue : {None, SyclQueue}, optional
         A SYCL queue to use for output array allocation and copying. The
         `sycl_queue` can be passed as ``None`` (the default), which means
@@ -1051,23 +1057,27 @@ def rand(d0, *dn, device=None, usm_type="device", sycl_queue=None):
     Returns
     -------
     out : dpnp.ndarray
-        Random values in a given shape.
-        Output array data type is :obj:`dpnp.float64` if device supports it, or :obj:`dpnp.float32` otherwise.
+        Random values in a given shape ``(d0, d1, ..., dn)``.
+        Output array data type is :obj:`dpnp.float64` if a device supports it,
+        or :obj:`dpnp.float32` type otherwise.
 
-    Examples
+    See Also
     --------
-    >>> s = dpnp.random.rand(3, 2)
+    :obj:`dpnp.random.random` : Return random floats in the half-open interval
+                                ``[0.0, 1.0)``.
+    :obj:`dpnp.random.random_sample` : Return random floats in the half-open
+                                       interval ``[0.0, 1.0)``.
+    :obj:`dpnp.random.uniform` : Draw samples from a uniform distribution.
 
-    See Also
+    Examples
     --------
-    :obj:`dpnp.random.random`
-    :obj:`dpnp.random.random_sample`
-    :obj:`dpnp.random.uniform`
+    >>> import dpnp as np
+    >>> s = np.random.rand(3, 2)
 
     """
 
     rs = _get_random_state(device=device, sycl_queue=sycl_queue)
-    return rs.rand(d0, *dn, usm_type=usm_type)
+    return rs.rand(*args, usm_type=usm_type)
 
 
 def randint(

dpnp/tests/test_sycl_queue.py

@@ -1079,24 +1079,27 @@ def test_vecdot(device, shape_pair):
 
 
 @pytest.mark.parametrize(
-    "func, kwargs",
+    "func, args, kwargs",
     [
-        pytest.param("normal", {"loc": 1.0, "scale": 3.4, "size": (5, 12)}),
-        pytest.param("rand", {"d0": 20}),
+        pytest.param("normal", [], {"loc": 1.0, "scale": 3.4, "size": (5, 12)}),
+        pytest.param("rand", [20], {}),
         pytest.param(
             "randint",
+            [],
             {"low": 2, "high": 15, "size": (4, 8, 16), "dtype": dpnp.int32},
         ),
-        pytest.param("randn", {"d0": 20}),
-        pytest.param("random", {"size": (35, 45)}),
+        pytest.param("randn", [], {"d0": 20}),
+        pytest.param("random", [], {"size": (35, 45)}),
+        pytest.param(
+            "random_integers", [], {"low": -17, "high": 3, "size": (12, 16)}
+        ),
+        pytest.param("random_sample", [], {"size": (7, 7)}),
+        pytest.param("ranf", [], {"size": (10, 7, 12)}),
+        pytest.param("sample", [], {"size": (7, 9)}),
+        pytest.param("standard_normal", [], {"size": (4, 4, 8)}),
         pytest.param(
-            "random_integers", {"low": -17, "high": 3, "size": (12, 16)}
+            "uniform", [], {"low": 1.0, "high": 2.0, "size": (4, 2, 5)}
         ),
-        pytest.param("random_sample", {"size": (7, 7)}),
-        pytest.param("ranf", {"size": (10, 7, 12)}),
-        pytest.param("sample", {"size": (7, 9)}),
-        pytest.param("standard_normal", {"size": (4, 4, 8)}),
-        pytest.param("uniform", {"low": 1.0, "high": 2.0, "size": (4, 2, 5)}),
     ],
 )
 @pytest.mark.parametrize(
@@ -1105,11 +1108,11 @@ def test_vecdot(device, shape_pair):
     ids=[device.filter_string for device in valid_devices],
 )
 @pytest.mark.parametrize("usm_type", ["host", "device", "shared"])
-def test_random(func, kwargs, device, usm_type):
+def test_random(func, args, kwargs, device, usm_type):
     kwargs = {**kwargs, "device": device, "usm_type": usm_type}
 
     # test with default SYCL queue per a device
-    res_array = getattr(dpnp.random, func)(**kwargs)
+    res_array = getattr(dpnp.random, func)(*args, **kwargs)
     assert device == res_array.sycl_device
     assert usm_type == res_array.usm_type
 
@@ -1121,7 +1124,7 @@ def test_random(func, kwargs, device, usm_type):
     kwargs["sycl_queue"] = sycl_queue
 
     # test with in-order SYCL queue per a device and passed as argument
-    res_array = getattr(dpnp.random, func)(**kwargs)
+    res_array = getattr(dpnp.random, func)(*args, **kwargs)
     assert usm_type == res_array.usm_type
     assert_sycl_queue_equal(res_array.sycl_queue, sycl_queue)
 
@@ -2057,7 +2060,6 @@ def test_broadcast_to(device):
         pytest.param("concatenate", [[1, 2], [3, 4]], [[5, 6]]),
         pytest.param("dstack", [[1], [2], [3]], [[2], [3], [4]]),
         pytest.param("hstack", (1, 2, 3), (4, 5, 6)),
-        pytest.param("row_stack", [[7], [1], [2], [3]], [[2], [3], [9], [4]]),
         pytest.param("stack", [1, 2, 3], [4, 5, 6]),
         pytest.param("vstack", [0, 1, 2, 3], [4, 5, 6, 7]),
     ],

dpnp/tests/test_usm_type.py

@@ -808,7 +808,6 @@ def test_broadcast_to(usm_type):
         pytest.param("concatenate", [[1, 2], [3, 4]], [[5, 6]]),
         pytest.param("dstack", [[1], [2], [3]], [[2], [3], [4]]),
         pytest.param("hstack", (1, 2, 3), (4, 5, 6)),
-        pytest.param("row_stack", [[7], [1], [2], [3]], [[2], [3], [9], [4]]),
         pytest.param("stack", [1, 2, 3], [4, 5, 6]),
         pytest.param("vstack", [0, 1, 2, 3], [4, 5, 6, 7]),
     ],

dpnp/tests/third_party/cupy/binary_tests/test_elementwise.py

@@ -4,6 +4,7 @@
 
 
 class TestElementwise(unittest.TestCase):
+
     @testing.for_int_dtypes()
     @testing.numpy_cupy_array_equal()
     def check_unary_int(self, name, xp, dtype):

dpnp/tests/third_party/cupy/binary_tests/test_packing.py

@@ -0,0 +1,85 @@
+import unittest
+
+import numpy
+import pytest
+
+import dpnp as cupy
+from dpnp.tests.third_party.cupy import testing
+
+pytest.skip(
+    "packbits() and unpackbits() are not supported yet", allow_module_level=True
+)
+
+
+class TestPacking(unittest.TestCase):
+
+    @testing.for_int_dtypes()
+    @testing.numpy_cupy_array_equal()
+    def check_packbits(self, data, xp, dtype, bitorder="big"):
+        # Note numpy <= 1.9 raises an Exception when an input array is bool.
+        # See https://github.com/numpy/numpy/issues/5377
+        a = xp.array(data, dtype=dtype)
+        return xp.packbits(a, bitorder=bitorder)
+
+    @testing.numpy_cupy_array_equal()
+    def check_unpackbits(self, data, xp, bitorder="big"):
+        a = xp.array(data, dtype=xp.uint8)
+        return xp.unpackbits(a, bitorder=bitorder)
+
+    def test_packbits(self):
+        self.check_packbits([0])
+        self.check_packbits([1])
+        self.check_packbits([0, 1])
+        self.check_packbits([1, 0, 1, 1, 0, 1, 1, 1])
+        self.check_packbits([1, 0, 1, 1, 0, 1, 1, 1, 1])
+        self.check_packbits(numpy.arange(24).reshape((2, 3, 4)) % 2)
+
+    def test_packbits_order(self):
+        for bo in ["big", "little"]:
+            self.check_packbits([0], bitorder=bo)
+            self.check_packbits([1], bitorder=bo)
+            self.check_packbits([0, 1], bitorder=bo)
+            self.check_packbits([1, 0, 1, 1, 0, 1, 1, 1], bitorder=bo)
+            self.check_packbits([1, 0, 1, 1, 0, 1, 1, 1, 1], bitorder=bo)
+            self.check_packbits(
+                numpy.arange(24).reshape((2, 3, 4)) % 2, bitorder=bo
+            )
+
+    def test_packbits_empty(self):
+        # Note packbits of numpy <= 1.11 has a bug against empty arrays.
+        # See https://github.com/numpy/numpy/issues/8324
+        self.check_packbits([])
+
+    def test_pack_invalid_order(self):
+        a = cupy.array([10, 20, 30])
+        pytest.raises(ValueError, cupy.packbits, a, bitorder="ascendant")
+        pytest.raises(ValueError, cupy.packbits, a, bitorder=10.4)
+
+    def test_pack_invalid_array(self):
+        fa = cupy.array([10, 20, 30], dtype=float)
+        pytest.raises(TypeError, cupy.packbits, fa)
+
+    def test_unpackbits(self):
+        self.check_unpackbits([])
+        self.check_unpackbits([0])
+        self.check_unpackbits([1])
+        self.check_unpackbits([255])
+        self.check_unpackbits([100, 200, 123, 213])
+
+    def test_unpack_invalid_array(self):
+        a = cupy.array([10, 20, 30])
+        pytest.raises(TypeError, cupy.unpackbits, a)
+        pytest.raises(TypeError, cupy.unpackbits, a.astype(float))
+
+    def test_pack_unpack_order(self):
+        for bo in ["big", "little"]:
+            self.check_unpackbits([], bitorder=bo)
+            self.check_unpackbits([0], bitorder=bo)
+            self.check_unpackbits([1], bitorder=bo)
+            self.check_unpackbits([255], bitorder=bo)
+            self.check_unpackbits([100, 200, 123, 213], bitorder=bo)
+
+    def test_unpack_invalid_order(self):
+        a = cupy.array([10, 20, 30], dtype=cupy.uint8)
+        pytest.raises(ValueError, cupy.unpackbits, a, bitorder="r")
+        pytest.raises(ValueError, cupy.unpackbits, a, bitorder=10)

dpnp/tests/third_party/cupy/core_tests/test_array_function.py

@@ -0,0 +1,64 @@
+import unittest
+
+import numpy
+import pytest
+
+import dpnp as cupy
+from dpnp.tests.third_party.cupy import testing
+
+pytest.skip(
+    "__array_function__ protocol is not supported", allow_module_level=True
+)
+
+
+class TestArrayFunction(unittest.TestCase):
+
+    @testing.with_requires("numpy>=1.17.0")
+    def test_array_function(self):
+        a = numpy.random.randn(100, 100)
+        a_cpu = numpy.asarray(a)
+        a_gpu = cupy.asarray(a)
+
+        # The numpy call for both CPU and GPU arrays is intentional to test the
+        # __array_function__ protocol
+        qr_cpu = numpy.linalg.qr(a_cpu)
+        qr_gpu = numpy.linalg.qr(a_gpu)
+
+        if isinstance(qr_cpu, tuple):
+            for b_cpu, b_gpu in zip(qr_cpu, qr_gpu):
+                assert b_cpu.dtype == b_gpu.dtype
+                testing.assert_allclose(b_cpu, b_gpu, atol=1e-4)
+        else:
+            assert qr_cpu.dtype == qr_gpu.dtype
+            testing.assert_allclose(qr_cpu, qr_gpu, atol=1e-4)
+
+    @testing.with_requires("numpy>=1.17.0")
+    def test_array_function2(self):
+        a = numpy.random.randn(100, 100)
+        a_cpu = numpy.asarray(a)
+        a_gpu = cupy.asarray(a)
+
+        # The numpy call for both CPU and GPU arrays is intentional to test the
+        # __array_function__ protocol
+        out_cpu = numpy.sum(a_cpu, axis=1)
+        out_gpu = numpy.sum(a_gpu, axis=1)
+
+        assert out_cpu.dtype == out_gpu.dtype
+        testing.assert_allclose(out_cpu, out_gpu, atol=1e-4)
+
+    @testing.with_requires("numpy>=1.17.0")
+    @testing.numpy_cupy_equal()
+    def test_array_function_can_cast(self, xp):
+        return numpy.can_cast(xp.arange(2), "f4")
+
+    @testing.with_requires("numpy>=1.17.0")
+    @testing.numpy_cupy_equal()
+    def test_array_function_common_type(self, xp):
+        return numpy.common_type(
+            xp.arange(2, dtype="f8"), xp.arange(2, dtype="f4")
+        )
+
+    @testing.with_requires("numpy>=1.17.0")
+    @testing.numpy_cupy_equal()
+    def test_array_function_result_type(self, xp):
+        return numpy.result_type(3, xp.arange(2, dtype="f8"))